​Written by a proven authorial team with internationalexperience, this hands-on road map takes you from the fundamentals of credit risk management to implementing proven strategies in a real-world environment using the SAS® credit risk management software. This latest addition enables you to:• Exercise proficiency in credit risk management, from applied theory to various real-life case studies• Build models from the ground up, as well as validate and stress-test existing models• Access exclusive, online materials and a supportive community

Credit risk analytics in R will enable you to build credit risk models from start to finish in the popular open source programming language R. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing.

This book has been written as a companion to Baesens, B., Roesch, D. and Scheule, H., 2016. Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. John Wiley & Sons.